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Coefficients of the CATE estimated with boosting, linear regression, two regression, contrast regression, random forest, generalized additive model
intxmean(
y,
trt,
x.cate,
x.init,
x.ps,
score.method = c("boosting", "gaussian", "twoReg", "contrastReg", "gam",
"randomForest"),
ps.method = "glm",
minPS = 0.01,
maxPS = 0.99,
initial.predictor.method = "boosting",
xvar.smooth.init,
xvar.smooth.score,
tree.depth = 2,
n.trees.rf = 1000,
n.trees.boosting = 200,
B = 1,
Kfold = 2,
plot.gbmperf = TRUE,
...
)Depending on what score.method is, the outputs is a combination of the following:
result.boosting: Results of boosting fit and best iteration, for trt = 0 and trt = 1 separately
result.gaussian: Linear regression estimator (beta1 - beta0); vector of length p.cate + 1
result.twoReg: Two regression estimator (beta1 - beta0); vector of length p.cate + 1
result.contrastReg: A list of the contrast regression results with 3 elements:
$delta.contrastReg: Contrast regression DR estimator; vector of length p.cate + 1
$sigma.contrastReg: Variance covariance matrix for delta.contrastReg; matrix of size p.cate + 1 by p.cate + 1
result.randomForest: Results of random forest fit and best iteration, for trt = 0 and trt = 1 separately
result.gam: Results of generalized additive model fit and best iteration, for trt = 0 and trt = 1 separately
best.iter: Largest best iterations for boosting (if used)
fgam: Formula applied in GAM when initial.predictor.method = 'gam'
warn.fit: Warnings occurred when fitting score.method
err.fit:: Errors occurred when fitting score.method
Observed outcome; vector of size n (observations)
Treatment received; vector of size n units with treatment coded as 0/1
Matrix of p.cate baseline covariates; dimension n by p.cate (covariates in the outcome model)
Matrix of p.init baseline covariates; dimension n by p.init
It must be specified when score.method = contrastReg or twoReg.
Matrix of p.ps baseline covariates (plus a leading column of 1 for the intercept);
dimension n by p.ps + 1 (covariates in the propensity score model plus intercept)
A vector of one or multiple methods to estimate the CATE score.
Allowed values are: 'boosting', 'gaussian', 'twoReg', 'contrastReg',
'randomForest', 'gam'. Default specifies all 6 methods.
A character value for the method to estimate the propensity score.
Allowed values include one of:
'glm' for logistic regression with main effects only (default), or
'lasso' for a logistic regression with main effects and LASSO penalization on
two-way interactions (added to the model if interactions are not specified in ps.model).
Relevant only when ps.model has more than one variable.
A numerical value (in `[0, 1]`) below which estimated propensity scores should be
truncated. Default is 0.01.
A number above which estimated propensity scores should be trimmed; scalar
A character vector for the method used to get initial
outcome predictions conditional on the covariates in cate.model
in score.method = 'twoReg' and 'contrastReg'. Allowed values include
one of 'gaussian' (fastest), 'boosting' (default) and 'gam'.
A vector of characters indicating the name of the variables used as
the smooth terms if initial.predictor.method = 'gam'. The variables must be selected
from the variables listed in init.model.
Default is NULL, which uses all variables in init.model.
A vector of characters indicating the name of the variables used as
the smooth terms if score.method = 'gam'. The variables must be selected
from the variables listed in cate.model.
Default is NULL, which uses all variables in cate.model.
A positive integer specifying the depth of individual trees in boosting
(usually 2-3). Used only if score.method = 'boosting' or
if score.method = 'twoReg' or 'contrastReg' and
initial.predictor.method = 'boosting'. Default is 2.
A positive integer specifying the number of trees. Used only if
score.method = 'randomForest'. Default is 1000.
A positive integer specifying the maximum number of trees in boosting
(usually 100-1000). Used only if score.method = 'boosting' or
if score.method = 'twoReg' or 'contrastReg' and
initial.predictor.method = 'boosting'. Default is 200.
A positive integer specifying the number of time cross-fitting is repeated in
score.method = 'twoReg' and 'contrastReg'. Default is 3.
A positive integer specifying the number of folds (parts) used in cross-fitting
to partition the data in score.method = 'twoReg' and 'contrastReg'.
Default is 6.
A logical value indicating whether to plot the performance measures in
boosting. Used only if score.method = 'boosting' or if score.method = 'twoReg'
or 'contrastReg' and initial.predictor.method = 'boosting'. Default is TRUE.
Additional arguments for gbm()